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Organisationseinheit der BAM
- 8.6 Faseroptische Sensorik (170) (entfernen)
We demonstrate (and are the first to do so) 63 km Brillouin Optical Frequency-Domain Analysis (BOFDA) for temperature and strain monitoring using a 100 km fiber loop. The use of BOFDA for long-range applications can be considered a novel approach, as previous investigations focused on the utilization of Brillouin Optical Time-Domain Reflectometry and Analysis (BOTDR and BOTDA, respectively). At 51.7 km, a 100 m hotspot (37 °C) was detected without using distributed Raman amplification or image processing.
We present, to our knowledge for the first time, a 100-km Brillouin Optical Frequency-Domain Analysis (BOFDA) employing a 200-km fiber loop. Compared to our previous publication, enhanced sensor length, sensor accuracy and spatial resolution are presented. The performance improvements are achieved by applying distributed Raman amplification (DRA) and a digital high-pass filter. We report on temperature measurements over sensing distances of 75 km and 100 km both with a 12.5-m spatial resolution. Temperature changes of 5 °C have been measured along 75 km sensing fiber. A temperature change of 30 °C has been detected at 99.5 km.
We present a temperature sensor based on a polymer exhibiting a Lower Critical Solution Temperature (LCST) in aqueous solution encapsulated in a capillary. Parameters are chosen such that the solution exhibits a cloud point in a temperature range of 30° C to 39°C. The characteristic of thermoresponsive polymers with an LCST, is that above that temperature phase separation of the polymer takes place which leads to a temperature-dependent formation of a cloudy suspension. An optical intensity measurement over the desired temperature range is established by an increase of optical attenuation inside the polymer solution caused by a rising temperature. For our purpose, the polymer capillary is connected to transmitter and receiver via a Polymer Optical Fibre (POF). Our intensity measurement is, to the best of our knowledge, a novel method and can be considered simple when compared to existing fibre-based temperature measurement techniques. Due to the lack of electrical components at the probe, this sensor is suitable for measurements in strong electromagnetic fields and environments for which flying sparks are hazardous, i.e., inflammable fluids or gases. Furthermore, all manufactured sensors share the same temperature dependence and, therefore, are well-suited for comparative measurement, e.g., flow measurement systems. With the given temperature range, a body temperature measurement is also suitable.
We demonstrate the use of a 3D printed radial collimator in X-ray powder diffraction and surface sensitive grazing incidence X-ray diffraction. We find a significant improvement in the overall Signal to background ratio of up to 100 and a suppression of more than a factor 3⋅10⁵ for undesirable Bragg reflections generated by the X-ray “transparent” windows of the sample environment.
The background reduction and the removal of the high intensity signals from the windows, which limit the detector’s dynamic range, enable significantly higher sensitivity in experiments within sample environments such as vacuum chambers and gas- or liquid-cells. Details of the additively manufactured steel collimator geometry, alignment strategies using X-ray fluorescence, and data analysis are also briefly discussed. The flexibility and affordability of 3D prints enable designs optimized for specific detectors and sample environments, without compromising the degrees of freedom of the diffractometer.
The influence of size effects on the thermoelectric properties of thin platinum films is investigated and compared to the bulk. Structural properties, like the film thickness and the grain size, are varied. We correlate the electron mean free path with the temperature dependence of the electrical conductivity and the absolute Seebeck coefficient SPt of platinum. A measurement platform was developed as a standardized method to determine SPt and show that SPt,film is reduced compared to SPt,bulk. Boundary and surface scattering reduce the thermodiffusion and the phonon drag contribution to SPt,film by nearly the same factor. We discuss in detail on behalf of a model, which describes the temperature dependence of the absolute Seebeck coefficient, the influence of size effects of electron-phonon and phonon-phonon interaction on SPt.
Monitoring of construction projects plays increasingly important role in the implementation of growing needs and requirements, especially in geologically difficult areas. This particularly concerns tunnelling and special civil engineering projects. The use of fiber optic sensors for structural health monitoring provides significant economic advantages regarding lower life-cycle costs of the civil infrastructure. We present our achievements in the development of distributed polymer optical fiber sensors based on Rayleigh and Brillouin scattering for early damage detection.
Acoustic emission testing
(2018)
The improvement potential of ultrafast all-optical switching by soliton self-trapping, using all-solid dual-core fibres with high index contrast, was analyzed numerically. The study of the femtosecond nonlinear propagation was performed based on coupled generalised nonlinear Schrödinger equations considering three fibre architectures: homogeneous cladding all-solid, photonic crystal air-glass, and photonic crystal all-solid. The structural geometries of all three architectures were optimised in order to support high-contrast switching performance in the C-band, considering pulse widths at the 100 fs level. Comparing the three structural alternatives, the lowest switching energies at common excitation parameters (1700 nm and 70 fs pulses) were predicted for the homogeneous cladding dual-core structure. Further optimization of the excitation wavelength and pulse width resulted in lower switching energies and simultaneous improvement of the switching contrasts at the combination of 1500 nm, 75 fs pulses and a fibre length of 43 mm. The spectral aspect in this optimised case expresses a broadband and uniform switching character with a span of over 200 nm and a contrast exceeding 30 dB at more frequency channels.
Application of Intensity-Based Coherent Optical Time Domain Reflectometry to Bridge Monitoring
(2022)
Although distributed fiber sensing techniques have been widely used in structural health monitoring, the measurement results of bridge monitoring, particularly under destructive testing, have rarely been reported. To the best of our knowledge, this paper is the first report of distributed vibration measurement results, which we obtained during a three-day destructive test on an abolished bridge. A coherent optical time domain reflectometry (COTDR) was used to acquire the vibration information while the bridge was being sawed. The obtained signal was analyzed in time and frequency domain. Some characteristics of the sawing-induced vibration were retrieved by the short-time Fourier transform; the vibration exhibited several high frequency components within the measured range up to 20 kHz and all the components appeared in the same time slot. Some unexpected signals were also detected. Thorough analysis showed that they are quite different from the sawing-induced vibration and are believed to originate from internal damage to the bridge (probably the occurrence of cracks).
In this study, a signal processing approach for heterodyne Փ-OTDR and C-OTDR systems that can obtain external perturbation and its frequency content in a faster way is proposed. We can detect vibrations with the same or better SNR using this processing approach, only with a single step and fast calculation. Fig. 12 shows the comparison of the normalized measurement time for the gamma matrix method and the conventional method throughout the three experiments. The speed of processing using a gamma matrix is ~35%–50% faster compared to a conventional method in high frequency test (PZT), low frequency test (walking) and street monitoring test. The processing speed in low frequency test is normally a bit lower than the similar high frequency one, since we must use higher number of time frames. Also, in street test we have higher speed because we can select wider gauges.
Mass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR) are critical components of every industrial chemical process as they provide information on the concentrations of individual compounds and by-products. These processes are carried out manually and by a specialist, which takes a substantial amount of time and prevents their utilization for real-time closed-loop process control. This paper presents recent advances from two projects that use Artificial Neural Networks (ANNs) to address the challenges of automation and performance-efficient realizations of MS and NMR. In the first part, a complete toolchain has been developed to develop simulated spectra and train ANNs to identify compounds in MS. In the second part, a limited number of experimental NMR spectra have been augmented by simulated spectra to train an ANN with better prediction performance and speed than state-of-theart analysis. These results suggest that, in the context of the digital transformation of the process industry, we are now on the threshold of a possible strongly simplified use of MS and MRS and the accompanying data evaluation by machine-supported procedures, and can utilize both methods much wider for reaction and process monitoring or quality control.
Mass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR) are critical components of every industrial chemical process as they provide information on the concentrations of individual compounds and by-products. These processes are carried out manually and by a specialist, which takes a substantial amount of time and prevents their utilization for real-time closed-loop process control. This paper presents recent advances from two projects that use Artificial Neural Networks (ANNs) to address the challenges of automation and performance-efficient realizations of MS and NMR. In the first part, a complete toolchain has been developed to develop simulated spectra and train ANNs to identify compounds in MS. In the second part, a limited number of experimental NMR spectra have been augmented by simulated spectra to train an ANN with better prediction performance and speed than state-of-theart analysis. These results suggest that, in the context of the digital transformation of the process industry, we are now on the threshold of a possible strongly simplified use of MS and MRS and the accompanying data evaluation by machine-supported procedures, and can utilize both methods much wider for reaction and process monitoring or quality control.
Mass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR) are valuable analytical and quality control methods for most industrial chemical processes as they provide information on the concentrations of individual compounds and by-products. These processes are traditionally carried out manually and by a specialist, which takes a substantial amount of time and prevents their utilization for real-time closed-loop process control. This paper presents recent advances from two projects that use Artificial Neural Networks (ANNs) to address the challenges of automation and performance-efficient realizations of MS and NMR. In the first part, a complete toolchain has been realized to develop simulated spectra and train ANNs to identify compounds in MS. In the second part, a limited number of experimental NMR spectra have been augmented by simulated spectra, to train an ANN with better prediction performance and speed than state-of-the-art analysis. These results suggest that, in the context of the digital transformation of the process industry, we are now on the threshold of a strongly simplified use of MS and MRS and the accompanying data evaluation by machine-supported procedures, and can utilize both methods much wider for reaction and process monitoring or quality control.
Industry 4.0 is all about interconnectivity, sensor-enhanced process control, and data-driven systems. Process analytical technology (PAT) such as online nuclear magnetic resonance (NMR) spectroscopy is gaining in importance, as it increasingly contributes to automation and digitalization in production. In many cases up to now, however, a classical evaluation of process data and their transformation into knowledge is not possible or not economical due to the insufficiently large datasets available. When developing an automated method applicable in process control, sometimes only the basic data of a limited number of batch tests from typical product and process development campaigns are available. However, these datasets are not large enough for training machine-supported procedures. In this work, to overcome this limitation, a new procedure was developed, which allows physically motivated multiplication of the available reference data in order to obtain a sufficiently large dataset for training machine learning algorithms. The underlying example chemical synthesis was measured and analyzed with both application-relevant low-field NMR and high-field NMR spectroscopy as reference method. Artificial neural networks (ANNs) have the potential to infer valuable process information already from relatively limited input data. However, in order to predict the concentration at complex conditions (many reactants and wide concentration ranges), larger ANNs and, therefore, a larger Training dataset are required. We demonstrate that a moderately complex problem with four reactants can be addressed using ANNs in combination with the presented PAT method (low-field NMR) and with the proposed approach to generate meaningful training data.
A novel DAS setup based on geometric phases in coherent heterodyne detection is applied for the first time to the characterisation of the Earth’s subsurface. In addition, an optimisation of the proposed setup in terms of its spatial resolution is also presented for the first time. The surface waves are generated by strong blasts of 25 kg of explosives at a dedicated test site. A 10 km dark fiber link in the vicinity of the test site connected to the test setup records the resulting strain signals. The spike-free and low-noise strain data thus obtained minimize post-processing requirements, making the setup a candidate for real-time seismic monitoring. An analysis of the dispersion characteristics of the generated surface waves is performed using a recently reported optimised seismic interferometric technique. Based on the dispersion characteristics, the shear wave velocities of the surface waves as a function of the depth profile of the Earth’s crust are determined using an optimised evolutionary algorithm.
We present our achievements in the development of distributed fiber optic sensing systems based on Brillouin optical frequency-domain analysis for structural health monitoring. The focus of the applications is on the gapless monitoring of geotechnical structures, large area infrastructures and electrical grids. The work includes the latest use of machine learning algorithms to reduce measurement time by coexistent increasing the measurement accuracy.
Glass-fibre-reinforced plastics (GFRP) are commonly used for the construction of pressure vessels and tanks for example in automotive and aerospace industries. Especially for pressure vessels used for the storage of natural gas or hydrogen with operation pressures up to 700 bar, an early material fatigue detection is of great interest. Measuring the distributed strain profile of GFRP structures helps to understand and detect material fatigue. In this article, we demonstrate the great potential of swept wavelength interferometry (SWI) based distributed strain sensing for the monitoring of pressure vessels made from GFRP. A resin transfer molding (RTM) process was used to manufacture GFRP-tube specimens. Commercial optical fibres with polyimid coating were glued to the surface externally in circumferential and axial direction. A cyclic load of up to 150 bar was applied to the samples using a servo-hydraulic test bench. Comparing the loaded and unloaded test conditions, we determined up to 2 % elongation in circumferential direction. We demonstrate reliable distributed strain measurements with sub-centimetre spatial resolution. By monitoring these high-resolution strain profiles, we were able to detect local material degradation that manifested itself as localized strain changes. Crucially, the material Degradation could be detected already after 75 % of dat the fatigue life before a crack appeared that led to leakage.
Glass-fibre-reinforced plastics (GFRP) are commonly used for the construction of pressure vessels and tanks for example in automotive and aerospace industries. Especially for pressure vessels used for the storage of natural gas or hydrogen with operation pressures up to 700 bar, an early material fatigue detection is of great interest. Measuring the distributed strain profile of GFRP structures helps to understand and detect material fatigue. In this article, we demonstrate the great potential of swept wavelength interferometry (SWI) based distributed strain sensing for the monitoring of pressure vessels made from GFRP. A resin transfer molding (RTM) process was used to manufacture GFRP-tube specimens. Commercial optical fibres with polyimid coating were glued to the surface externally in circumferential and axial direction. A cyclic load of up to 150 bar was applied to the samples using a servo-hydraulic test bench. Comparing the loaded and unloaded test conditions, we determined up to 2 % elongation in circumferential direction. We demonstrate reliable distributed strain measurements with sub-centimetre spatial resolution. By monitoring these high-resolution strain profiles, we were able to detect local material degradation that manifested itself as localized strain changes. Crucially, the material Degradation could be detected already after 75 % of dat the fatigue life before a crack appeared that led to leakage.
Phase-sensitive optical time domain reflectometry (φOTDR) is an excellent distributed fiber sensing technique and has been applied in various areas. Its noise is however never been comprehensively studied to the best of our knowledge. The different detection noise sources in such a sensing system are thoroughly investigated. The impacts of thermal noise, shot noise and the beat between signal and the amplified spontaneous emission from a pre-amplifier have been theoretically and experimentally demonstrated. Due to the random nature of the φOTDR signal, the detection noise demonstrates distinct features at different fiber positions in a single measurement. The theoretical analysis and the experimental result explicitly affirm most of the fiber sections, and the difference at some positions may be explained by ambient noise.
We compare four different sensing solutions suitable for distributed fiber optic humidity sensing in per uorinated graded-index polymer optical fibers (PFGI-POFs). Compared to silica fbers, polymer optical fibers over advantageous beneffits including signifficantly higher break down strain, fracture toughness and humidity sensitivity. Various humidity-related effects in PFGI-POFs have been reported in the last years including measured attenuation and length changes as well as Brillouin frequency and Bragg wavelength shifts. The four aforementioned methods could serve as a basis for distributed and quasi-distributed humidity sensing and are described here closely with an emphasis on plausible cross effects to temperature and strain. The main focus of this paper lies on the comparison of four approaches with regard to method complexity, sensitivity to humidity, spatial resolution, real-time capability and effort to compensate for cross effects.
We compare four different sensing solutions suitable for distributed fiber optic humidity sensing in per uorinated graded-index polymer optical fibers (PFGI-POFs). Compared to silica fbers, polymer optical fibers over advantageous beneffits including signifficantly higher break down strain, fracture toughness and humidity sensitivity. Various humidity-related effects in PFGI-POFs have been reported in the last years including measured attenuation and length changes as well as Brillouin frequency and Bragg wavelength shifts. The four aforementioned methods could serve as a basis for distributed and quasi-distributed humidity sensing and are described here closely with an emphasis on plausible cross effects to temperature and strain. The main focus of this paper lies on the comparison of four approaches with regard to method complexity, sensitivity to humidity, spatial resolution, real-time capability and effort to compensate for cross effects.
In the presented work, the potential of fiber-optic distributed acoustic sensing (DAS) for detection of small gas pipeline leaks (<1%) is investigated. Helical wrapping of the sensing fiber directly around the pipeline is used to increase the system sensitivity for detection of weak leak-induced vibrations. DAS measurements are supplemented with reference accelerometer data to facilitate analysis and interpretation of recorded vibration signals. The results reveal that a DAS system using direct fiber application approach is capable of detecting pipeline natural vibrations excited by the broadband noise generated by the leaking medium. In the performed experiment, pipeline vibration modes with acceleration magnitudes down to single ug were detected. Simple leak detection approach based on spectral integration of time-averaged DAS signals in frequency domain was proposed. Potential benefits and limitations of the presented monitoring Approach were discussed with respect to its practical applicability. We demonstrated that the approached is potentially capable of detection and localization of gas pipeline leaks with leak rates down to 0.1% of the pipeline flow volume and might be of interest for monitoring of short- and medium-length gas pipelines.
One of the biggest problems in high-voltage silicone rubber insulation cable accessories is the damage on electrical treeing, initiated by partial discharges. The electrical treeing starts at unavoidable imperfections inside the material or at interfaces. The damage is usually irreversible and leads around the starting points to a partial destruction of the material. To prolong the lifetime and thereby to increase the assurance of the structural component commonly, for mechanical improvement constituted filler is modified to obtain a self-healing silicone rubber. Damage mechanism is analysed to address the filler to the damage mode.
We propose a method based on artificial neural networks to extract strain information from wavelength-scanning coherent optical time domain reflectometry (C-OTDR) data. Our neural network algorithm performs more than two orders of magnitude faster than the conventional approach.
This is due to the highly parallel evaluation of the neural networks on a GPU accelerated computer and the fact that conventional correlation and interpolation analysis needs many Iteration steps. This opens the way for real-time C-OTDR strain sensing because the neural Network strain predictions require less time than the measurements themselves. Real-time data Analysis enables long-term sensing e.g. in structural health monitoring, because the large amount of raw data does not have to be stored but can immediately be reduced to the strain data of interest.
A Kramers-Kronig (KK) receiver is applied to a phase-sensitive optical time domain reflectometry based on direct detection. An imbalanced Mach-Zehnder interferometer with a 2× 2 coupler is used in sensing system to encode the phase information into optical intensity. The directly obtained signal is treated as the in-phase component, and the KK receiver provides the quadrature component by Hilbert transform of the obtained signal, so that the optical phase can be retrieved by IQ demodulation. The working principle is well explained, and the obtained phase variance is theoretically analyzed. The experiment demonstrates the functionality of the sensor and validates the theoretical analysis.
A novel distributed acoustic sensing technique is proposed that exploits both phase and amplitude of the Rayleigh backscattered light to quantify the environmental variation. The system employs a wavelength-scanning laser and an imbalanced Mach-Zehnder interferometer to acquire the reflection spectra and the phase of the detected light, respectively. Fading-free and low-frequency measurements are realized via the crosscorrelation of the reflection spectra. The discrete crosscorrelation is used to circumvent the nonlinear frequency sweeping of the laser. Based on the phase of the backscattered light, it is possible to quantify fast environmental variations. The whole system requires no hardware modification of the existing system and its functionality is experimentally validated. The proposed system has the potential to monitor ground motion/movement at very low frequency band like subsidence around mining areas and at high frequency band like earthquakes and vibrations induced by avalanches.
We present our research on the development of a Brillouin optical frequency-domain analysis (BOFDA) using a perfluorinated graded-index polymer optical fiber (PFGI-POF) as a sensing fiber. The described works include investigations both on the selection of the setup components with respect to mode coupling effects and on the impact of the humidity cross-sensitivity. The suitability of the developed POF-based BOFDA is proved by a distributed measurement on an 86 m long PFGI-POF recorded with the spatial resolution of 6 m.
We present our research on the development of a Brillouin optical frequency-domain analysis (BOFDA) using a perfluorinated graded-index polymer optical fiber (PFGI-POF) as a sensing fiber. The described works include investigations both on the selection of the setup components with respect to mode coupling effects and on the impact of the humidity cross-sensitivity. The suitability of the developed POF-based BOFDA is proved by a distributed measurement on an 86 m long PFGI-POF recorded with the spatial resolution of 6 m.
The substantial progresses in fiber-optic communications in combination with the increasing economic and political interest in structural health monitoring have led to a commercial establishment of distributed Brillouin sensing. The sensor systems, mainly based on the time-domain techniques, have been successfully implemented in the areas such as pipeline leak detection, geohazard effects, and ground movement detection.
This chapter introduces a further advancement in the area of Brillouin sensing in the frequency domain. The so-called Brillouin optical frequency-domain analysis (BOFDA) offers crucial perspectives in terms of dynamic range and cost efficiency.
The main principle of the frequency-domain approach takes advantage of the reversibility between the time and frequency domain given by a Fourier transform in the analysis of linear systems. The hereby presented overview gives a summary of the benefits and challenges of frequency-domain measurements closely tied to the narrowband recording of the complex transfer function. This function relates the counterpropagating pump and probe laser light along the sensor fiber providing the pulse response of the measurement system by applying the inverse Fourier transform (IFT). The strain or temperature distribution can be then determined from the retrieved Brillouin frequency shift (BFS) profile along the fiber.
In this thesis, a distributed Brillouin sensor in perfluorinated polymer optical fibers utilizing BOFDA is presented. These commercially available polymer fibers offer beneficial characteristics for sensing applications such as higher break down strain up to 100 %, minimal bending radii below 2 mm, higher sensitivity to temperature and lower sensitivity to strain compared to their silica equivalent.
The chosen wavelength of operation at 1319 nm corresponds to lower fiber propagation loss (< 37 dB/km) compared to other approaches at 1550 nm (150 - 250 dB/km). A 86 m PFGI-POF was successfully measured by BOFDA with spatial resolution of 3.4 m.
The findings related to humidity influences can serve as a basis for future distributed humidity sensors not only limited to stimulated Brillouin backscattering.
In this work, we present our results achieved in several research activities for development of distributed fiber optic radiation sensors using glass and polymer optical fibers. The findings show that both the measurement of the radiation-induced attenuation (RIA) along the entire sensing fiber and the accompanying change in the refractive index of the fiber core can be used for distributed radiation monitoring.
In this work, we present our results achieved in several research activities for development of fiber optic dosimeters. The findings show that both the measurement of the radiation-induced attenuation (RIA) along the entire sensing fiber and the accom panying change in the refractive index of the fiber core can be used for distributed radiation monitoring in the kGy and MGy range, respectively. Depending on the fiber type and material the RIA shows varying response to dose rates, environmental temperatures and the wavelength of the laser source used.
Division 8.6's competencies and work focus areas are shortly outlined and investigations related to pipeline and submarine power cable monitoring using different distributed fiber optic sensing techniques are presented with some results. Furthermore, BAM 8.6 expertise in the field of sensor application, embedding and integration is shown.
Distributed fiber optic sensing (DFOS) technique is a promising and robust non-destructive testing tool that can seamlessly acquire environmental conditions over large scales. Therefore, it has found extensive applications in structural health monitoring. Its appeal for monitoring underground facilities lies in the intrinsic properties of the optical fiber, such as immunity to magnetic interference, small size, chemical inertia, etc. This paper provides a concise overview of DFOS applications in underground facility monitoring. Following a brief introduction to the working principle of the DFOS technique, various examples are provided to demonstrate how distributed fiber sensors contribute to monitoring underground facilities. The paper presents unpublished field test results with an emphasis in the energy sector, including monitoring gas storage facilities, geothermal reservoir exploration, and ground movement detection. Furthermore, the paper identifies several directions for enhancing the DFOS system.
Distributed fiber optic sensors for structural health monitoring of composite pressure vessels
(2024)
In diesem Beitrag geben wir einen umfassenden Überblick über unsere Forschung auf dem Gebiet der verteilten faseroptischen Sensorik für die strukturelle Zustandsüberwachung von Wasserstoffdruckbehältern aus Verbundwerkstoffen. Insbesondere zeigen wir, wie die Integration von faseroptischen Sensoren in Druckbehälter aus Verbundwerkstoffen die Sicherheit erhöht und gleichzeitig die Wartungskosten senkt. Die geringe Größe von Lichtwellenleitern ermöglicht ihre Integration in Verbundwerkstoffstrukturen während des Herstellungsprozesses, wodurch eine kontinuierliche Überwachung sowie eine präzise Erkennung und Lokalisierung von Strukturschäden während des Betriebs der Druckbehälter ermöglicht wird. Wir erörtern auch das Potenzial modernster Signalverarbeitungsmethoden und des maschinellen Lernens für die Weiterentwicklung der vorausschauenden Instandhaltung. Die von uns vorgestellten Anwendungen von faseroptischen Sensoren zeigen, dass sie einen wichtigen Beitrag zur Energiewende hin zu erneuerbaren Energien leisten können.
Along temperature, humidity is one of the principal environmental factors that plays an important role in various application areas. Presented work investigates possibility of distributed fiberoptic humidity monitoring based on humidity-induced strain measurement in polyimide (PI)-coated optical fibers. Characterization of relative humidity (RH) and temperature response of four different commercial PI- and one acrylate-coated fiber was performed using optical backscattering reflectometry (OBR). The study addresses issues of temperature-humidity cross sensitivity, fiber response stability, repeatability, and the influence of annealing. Acrylate-coated fiber exhibited rather unfavorable nonlinear RH response with strong temperature dependence, which makes it unsuitable for humidity sensing applications. On the other hand, humidity response of PI-coated fibers showed good linearity with fiber sensitivity slightly decreasing at rising temperatures. In the tested range, temperature sensitivity of the fibers remained humidity independent. Thermal annealing was shown to considerably improve and stabilize fiber RH response. Based on performed analysis, a 20 m sensor using the optimal PI-coated fibers was proposed and constructed. The sensor uses dual sensing fiber configuration for mutual decoupling and simultaneous measurement of temperature and RH variations. Using OBR, distributed dual temperature-RH monitoring with cm spatial resolution was demonstrated for the first time.
Distributed Fibre Optic Sensing for Monitoring and Testing of Industrial and Civil Infrastructures
(2018)
Fibre optic sensors enable the gathering of sensory information in environments where conventional sensors fail or simply can not be utilised. Their small size enables their use in confined spaces as well as their embedding or integration in components, structures or composite materials while offering, depending on the approach, high sensitivity, very high spatial resolution and, especially, the possibility to detect or measure physical quantities in a distributed way, meaning spatially continuous sensing over very long distances.
Those distributed fibre optic sensors (DFOS) provide access to sensing information from different locations along the sensing fibre without the need for technologically challenging multiplexing of many point sensors and the subsequent sensor data fusion. In this way, DFOS are excellently suited as smart sensors for monitoring or testing purposes as the spatially distributed sensing data make available spatially resolved measurand profiles without a gap. Furthermore, the possible embedding or integration of DFOS could facilitate the development of smart structures or smart components. In particular distributed vibration sensing (DVS) and distributed temperature sensing (DTS) are also ideally suited for condition monitoring of machinery, industrial installations or extensive infrastructure, also, on-line real-time assembly or component testing in production lines, e.g. for quality control, is possible.
In this talk, we will shortly outline the underlying principles of some fibre optic sensing approaches, explain their fundamental differences in mode of operation and performance, and discuss their applicability and limitations. Moreover, we will report on a subset of current and previous applied research projects at BAM, investigating different areas of use for distributed fibre optic sensors in particular.
Distributed humidity fiber-optic sensor based on BOFDA using a simple machine learning approach
(2022)
We report, to our knowledge for the first time, on distributed relative humidity sensing in silica polyimide-coated optical fibers using Brillouin optical frequency domain analysis (BOFDA). Linear regression, which is a simple and well-interpretable algorithm in machine learning and statistics, is utilized. The algorithm is trained using as features the Brillouin frequency shifts and linewidths of the fiber’s multipeak Brillouin spectrum. To assess and improve the effectiveness of the regression algorithm, we make use of machine learning concepts to estimate the model’s uncertainties and select the features that contribute most to the model’s performance. In addition to relative humidity, the model is also able to simultaneously provide distributed temperature information addressing the well-known cross-sensitivity effects.
We present a preliminary investigation on distributed humidity monitoring during the drying process of concrete based on an embedded polymer optical fiber (POF). The water dissipated into the POF changes several properties of the fiber such as refractive index, scattering coefficient and attenuation factor, which eventually alters the Rayleigh backscattered light. The optical time Domain reflectometer (OTDR) technique is performed to acquire the backscattered signal at the wavelengths 650 nm and 500 nm, respectively. Experimental results show that the received signal increases at 650 nm while the fiber attenuation factor clearly increases at 500 nm, as the concrete dries out. In the hygroscopic range, the information retrieved from the signal change at 650 nm agrees well with the measurement result of the electrical humidity sensors also embedded in the concrete sample.